Metadata-Version: 1.1
Name: goldilocks
Version: 0.0.7
Summary: Locating genomic regions that are "just right".
Home-page: https://github.com/samstudio8/goldilocks
Author: Sam Nicholls
Author-email: sam@samnicholls.net
License: UNKNOWN
Description: ==========
        Goldilocks
        ==========
        
        .. image:: https://badge.fury.io/py/goldilocks.png
            :target: http://badge.fury.io/py/goldilocks
        
        .. image:: https://travis-ci.org/SamStudio8/goldilocks.png?branch=master
                :target: https://travis-ci.org/SamStudio8/goldilocks
        
        .. image:: https://coveralls.io/repos/SamStudio8/goldilocks/badge.png?branch=master
                :target: https://coveralls.io/r/SamStudio8/goldilocks
        
        Locating genomic regions that are "just right".
        
        * Documentation: http://goldilocks.readthedocs.org.
        
        
        What is it?
        -----------
        
        **Goldilocks** is a Python package providing functionality for locating 'interesting'
        genomic regions for some definition of 'interesting'. You can import it to your
        scripts, pass it sequence data and search for subsequences that match some criteria
        across one or more samples.
        
        Goldilocks was developed to support our work in the investigation of quality
        control for genetic sequencing. It was used to quickly locate
        regions on the human genome that expressed a desired level of variability,
        which were "just right" for later variant calling and comparison.
        
        The package has since been made more flexible and can be used to find regions
        of interest based on other criteria such as GC-content, density of target k-mers,
        defined confidence metrics and missing nucleotides.
        
        
        What can I use it for?
        ----------------------
        
        Given some genetic sequences (from one or more samples, comprising of one or more
        chromosomes), Goldilocks will shard each chromosome in to subsequences of a
        desired size which may or may not overlap as required. For each chromosome from
        each sample, each subsequence or 'region' is passed to the user's chosen strategy.
        
        The strategy simply defines what is of interest to the user in a language that
        Goldilocks can understand. Goldilocks is currently packaged with the following
        strategies:
        
        ==========================      ==================
        Strategy                        Census Description
        ==========================      ==================
        GCRatioStrategy                 Calculate GC-ratio for subregions across the
                                        genome.
        NucleotideCounterStrategy       Count given nucleotides for subregions across
                                        the genome.
        KMerCounterStrategy             Search for one or more particular k-mers of
                                        interest of any and varying size in subregions
                                        across the genome.
        VariantCounterStrategy          Given a list of SNP locations, calculate
                                        variant density of subregions over the genome.
        ==========================      ==================
        
        Once all regions have been 'censused', the results may be sorted by one of four
        mathematical operations: `max`, `min`, `median` and `mean`. So you may be interested
        in subregions of your sequence(s) that feature the most missing nucleotides, or
        subregions that contain the mean or median number of SNPs or the lowest GC-ratio.
        
        
        Why should I use it?
        --------------------
        
        Goldilocks is hardly the first tool capable of calculating GC-content across a
        genome, or to find k-mers of interest, or SNP density, so why should you use it
        as part of your bioinformatics pipeline?
        
        Whilst not the first program to be able to conduct these tasks, it is the first
        to be capable of doing them all together, sharing the same interfaces. Every strategy
        can quickly be swapped with another by changing one line of your code. Every strategy
        returns regions in the same format and so you need not waste time munging data to
        fit the rest of your pipeline.
        
        Strategies are also customisable and extendable, those even vaguely familiar with
        Python should be able to construct a strategy to meet their requirements.
        
        Goldilocks is maintained, documented and tested, rather than that hacky perl
        script that you inherited years ago from somebody who has now left your lab.
        
        
        Requirements
        ------------
        To use;
        
        * numpy
        
        To test;
        
        * tox
        * pytest
        
        For coverage;
        
        * nose
        * python-coveralls
        
        Installation
        ------------
        
        ::
        
            $ pip install goldilocks
        
        
        Citation
        --------
        
        Citation pending...
        
        
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        History
        =======
        
        0.0.6 (2014-06-23)
        ------------------
        * `Goldilocks.sorted_regions` stores a list of region ids to represent the result
          of a sorting operation following a call to `query`.
        * Regions in `Goldilocks.regions` now always have a copy of their "id" as a key.
        * `__check_exclusions` now accepts a `group` and `track` for more complex
          exclusion-based operations.
        * `region_group_lte` and `region_group_gte` added to usable exclusion fields to
          remove regions where the value of the desired group/track combination is
          less/greater than or equal to the value of the group/track set by the
          current `query`.
        * `query` now returns a new `Goldilocks` instance, rather than a `CandidateList`.
        * `Goldilocks.candidates` property now allows access to regions, this property
          will maintain the order of `sorted_regions` if it has one.
        * `export_meta` now allows `group=None`
        * `CandidateList` class deleted.
        * Test data that is no longer used has been deleted.
        * Scripts for generating test data added to `test_gen/` directory.
        * Tests updated to reflect the fact `CandidateList` lists are no longer returned
          by `query`.
        * `_filter` is to be deprecated in favour of `query` by 0.0.7
        
        Beta (2014-10-08)
        ---------------------
        * Massively updated! Compatability with previous versions very broken.
        * Software retrofitted to be much more flexible to support a wider range of problems.
        
        0.0.2 (2014-08-18)
        ---------------------
        
        * Remove incompatible use of `print`
        
        0.0.1 (2014-08-18)
        ---------------------
        
        * Initial package
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
